01 The Project

DART aims to present to the ATM community an understanding on what can be achieved today in trajectory prediction by using data-driven models, also accounting for network complexity effects. It is expected that data-driven techniques help to improve the performance and accuracy of predictions by complementing classical model-based prediction approaches. These improved predictions will enable advanced collaborative decision making processes, which finally will lead to a more efficient ATM procedures.

DART will explore the applicability of a collection of data mining, machine learning and agent-based models and algorithms to derive a data-driven trajectory prediction capability, accounting also for ATM network complexity effects.